Top 5 Machine Learning Courses for 2021

In the digital age, we have more data sets than ever before – our search engine history, online shopping orders, social media photographs. Machines are collecting, analysing, and learning from data sets we create through simply living our life in modernity and engaging with technology.

Machine Learning is a method of data analysis that automates analytical model building, utilising algorithms to recognise information in data sets, without the computer being programmed to do so. The more data the machine has, the stronger its autonomous decision-making becomes.

The importance of knowledge around Machine Learning skills has become clear as personalised elements of technology development and will be even more important in the future. Our selected top 5 Machine Learning Courses provides you with insight as to how.

Our top 5 ML courses include:

• Amazon Web Services (AWS) Certified Machine Learning - Specialty
• Amazon Web Services (AWS) Certified Big Data - Specialty
• Microsoft Certified Azure Data Engineer Associate
• Logical Operations Big Data Essentials Bootcamp
• Google Cloud Certified Professional Data Engineer

 

Check out the following blog for more information on how to start your ML career:

 

AmazonWeb Services (AWS) Certified Machine Learning - Specialty

Duration: 3 days

Exam: MLS-C01

 

By getting achieving AWS' Certified Machine Learning - Speciality certification, you have the skills to support the growth of your business by developing machine learning models, feature engineering, data analysing and hyperparameter optimisation.

 

You'll be able to deter from external threats by choosing appropriate ML approaches and AWS services to implement ML solutions in the AWS Cloud. 

 

Prerequisites: Before attending this accelerated course, it's recommended you possess the following:

• One to two years' experience developing, architecting, or running ML/deep learning workloads on the AWS Cloud
• The ability to express the intuition behind basic ML algorithms
• Experience performing basic hyperparameter optimization
• Experience with ML and deep learning frameworks
• The ability to follow model-training best practices
• The ability to follow deployment and operational best practices

Amazon Web Services (AWS) Certified Big Data – Specialty

Duration: 4 days
Exam: BDS-C00

On this accelerated speciality course, you will learn how to use cloud-based big data solutions, use visualisation software, control in-memory analytics and manage and process data.

Prerequisites: Before attending this accelerated course, you should have a minimum of two years' experience using AWS technology and be able to:

• Independently define AWS architecture and services and understand how they integrate with each other
• Define and architect AWS big data services and explain how they fit in the data lifecycle of collection, ingestion, storage, processing, and visualisation

You should also possess at least five years' experience in a data analytics field and understand:

• How to control access to secure data
• The frameworks that underpin large scale distributed systems like Hadoop/Spark and MPP data
• warehouses
• The tools and design platforms that allow processing of data from multiple heterogeneous sources with difference frequencies (batch/real-time)
• How to design a scalable and cost-effective architecture to process data

 

 Microsoft Certified Azure Data Engineer Associate

Duration: 4 days
Exam: DP-200 and DP-201

Through this accelerated Microsoft Certified Azure Data Engineer Associate course, you’ll be certified to manage data security and troubleshoot Azure data solutions. You’ll learn how to design software to protect your business as well as implement speedy disaster recovery. 

As a Microsoft Azure Data Engineer Associate, you'll be able to manage and design both data security and data solutions as well as data processing.

Prerequisites: Before attending this course, you should have technical knowledge equivalent to Azure Fundamentals certification.

 

Logical Operations Big Data Essentials Bootcamp

Duration: 3 Days
Exam: There is no exam for this accelerated course.

This course is perfect for developers, architects or technical managers looking to implement and architect Big Data systems. In 3 days, you’ll learn how to architect and run Big Data systems with full-time lab access so you can learn whenever suits you.

Prerequisites: Before attending this course, you should be familiar with at least one programming language and be comfortable working with a command-line interface.

 

Google Cloud Certified Professional Data Engineer

Duration: 5 days
Exam: Google Cloud Certified Professinal Data Engineer exam

This course is ideal for an experienced developer, responsible for managing big data transformations. In 3 days, you’ll learn the skillset of a professional data engineer, including: how to design and build data processing systems and enable data-driven decision making.

Prerequisites: There are no formal prerequisites for this course. However, Google recommends you have:

• At least three years of industry experience, including one or more years of designing and managing solutions using GCP
• One years' experience in one or more of the following:
• A common query language such as SQL Extract
• Data modelling
• Machine learning
• A common programming language such as Python

 

Machine Learning in the Real World

It is no exaggeration stating we encounter Machine Learning technology daily. Our banks monitor the safety of transactions, streaming sites suggest what video you would like to watch next, and your email provider is sieving through your Inbox – separating spam so you do not have to. 

Data output from machine learning is more personalised than standard data sets and has slowly become a useful tool many people have not even noticed.

A simple, day-to-day encounter of Machine Learning we may have overlooked is search engine results. Search engines use complex algorithms, historical search data to determine complex search queries.

For example, searching for ‘nails’ on a search engine provides two potential options, the search engine needs to understand if the user needs to see some inspirational images for their nail varnish or a list of hardware stores near them.

Using complex Machine Learning algorithms can cleverly predict which kind of ‘nail’ the user is searching for, without any extra detail given in the search bar. If the search engine shows an undesirable result, the user may not select what the computer predicted. 

This user outcome is new data that the computer can use for future decision-making - smart stuff!

 

What Skillsets are Needed for Career in Machine Learning?

Five skills vital to support a career in Machine Learning are:

1) Understanding of core concepts in computer science

Even the basic principles of Machine Learning require the need for an in-depth understanding of multiple computer science concepts. You will need to understand a range of software engineering principles to build an efficient and scalable application

2) Data modelling and analysis

Being able to spot patterns and dissect data sets is crucial to allow you to develop the data bases and models needed for your machine to continually learn and adapt

3) Understanding of algorithm theory and machine learning algorithms

A successful career in machine learning, not only requires an understanding of advanced algorithms like linear regression, gradient descent and quadratic programming, but when to apply them.

4) Applied mathematics, particularly statistics

A thorough understanding of probability and statistics will be essential to help you to build your career. Understanding the variety of algorithms available to you will help your machine in their decision-making process.

5) Knowledge of programming languages

Being able to program complex solutions using one of these programming languages would be recommended: Python, Scala, Java, R, JavaScript, Lisp or C++.

 

Top Career Paths in Machine Learning

Google, Facebook and Uber all use Machine Learning elements in their products. Working with some of the most successful companies in tech brings unbelievable opportunities. 

The size of these companies means they can develop stronger AI tech that you could be at the centre of. 

From Big Data Engineer to Machine Learning Engineer, the roles within Machine Learning will undoubtedly continue to flourish alongside science.

Machine Learning is the Artificial Intelligence (AI) at the heart of so many developing technologies we connect to. There are various ways to gain skills in Machine Learning and our Machine Learning Courses are the best way to get started. They are flexible, time-efficient and offer diverse learning opportunities. 

Develop your Machine Learning understanding and career through our courses today.

 

Average Salary Within Machine Learning

A starting salary for a career in Machine Learning is generous, estimated to be around £35,000 p.a. With time and experience, the average income of Machine Learning Engineers in the UK tots up to (at least) £53,000 p.a. This is a career opportunity with plenty to offer.

Some of the largest global companies utilise Machine Learning at the forefront of their products - machines are learning! Top companies across the world are noticing the true potential of Machine Learning and providing career opportunities to match.

 

Get ML Certified 

Firebrand Training are an official training provider for an array of world-renowned certification bodies, including Microsoft, Amazon Web Services (AWS) and Logical Operations. 

Getting certified with us means you'll get acess to official exam and courseware, learn from certified instructors, and train in a distraction-free environment. 

We offer an array of ML courses - this is just our top 5! Follow the link below to see what else we have on offer.